Simulations
Simulate responses and response times based on known \(\theta\)’s, \(\beta\)’s and \(a\)-parameters. Look how wel we can recover the \(a\)-parameter.
Look at BIAS and SEM.
---
title: "Item Discrimination in Math Garden"
author: "Sharon Klinkenberg"
output:
flexdashboard::flex_dashboard:
logo: http://shklinkenberg.github.io//statistics-lectures/template/logo_uva.png
orientation: col
social: menu
source_code: embed
vertical_layout: fill
navbar:
- { icon: "ion-android-contact", title: "Contact", href: "http://www.uva.nl/en/profile/k/l/s.klinkenberg/s.klinkenberg.html", align: right }
- { icon: "fa-download", title: "Poster", href: "poster.pdf", align: right }
---
```{r setup, include=FALSE}
library(flexdashboard)
```
Inputs {.sidebar}
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Research
The aim of this study is to find a feasable method to determine item discrimination values within the Math Garden to detect deviant items.
__What is the problem__
* Sparse data
* Scaling
* Identifiability
__Pragmatic solution__
So there are some fundamental problems in estimating a. But can we at least have some pragmatic solution?
__For fancy visuals see QR code__
https://goo.gl/a6ch6A
Column {data-width=500}
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### Discrimination

### Item ratings

### User Ratings

Column
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### Method
__Simulations__
Simulate responses and response times based on known $\theta$'s, $\beta$'s and $a$-parameters. Look how wel we can recover the $a$-parameter.
* Simulate full data
* Estimate with LTM
* GLM
* Newton-Rapson
* Simulate sparse data
* LTM
* GLM
* Newton-Rapson
Look at BIAS and SEM.
### Proliminary Results

### TO-DO {data-height=200}
* $a = 1$
* $a \sim U(0,3)$
* $a \sim U(-.5,3)$
* Apply NR in Math Garden
* Inactivate bad items
* Apply to real data